Conversational programming

ABSTRACT

Methods and systems for providing rich semantic feedback to programmers by executing programs, or parts of programs, in data contexts relevant to the programmer are provided. According to one embodiment, software code associated with one or more of multiple programming building blocks is enabled to be concurrently edited and executed within a programming environment. A conversational programming agent of the programming environment receives (i) information regarding the programming building blocks and (ii) information indicative of a current situation relating to the programming building blocks. The conversational programming agent evaluates the programming building blocks based on the current situation. Then, detection of one or more logical errors in one or more of the programming building blocks is facilitated by the conversational programming agent proactively providing semantic feedback regarding those of the programming building blocks to which the current situation is relevant to the programmer based on results of the evaluation.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/012,465, filed Jan. 24, 2011, now U.S. Pat. No. 8,645,914, whichclaims the benefit of priority to U.S. Provisional Patent ApplicationNo. 61/297,701, filed on Jan. 22, 2010, both of which are herebyincorporated by reference in their entirety for all purposes.

COPYRIGHT NOTICE

Contained herein is material that is subject to copyright protection.The copyright owner has no objection to the facsimile reproduction ofthe patent disclosure by any person as it appears in the Patent andTrademark Office patent files or records, but otherwise reserves allrights to the copyright whatsoever. Copyright© 2010-2014, AgentSheets,Inc.

BACKGROUND

1. Field

Embodiments of the present invention relate generally to computerprogramming, game design and computational thinking. More particularly,embodiments of the present invention relate to methods of providing richsemantic feedback to programmers by evaluating rules in a behavioreditor responsive to hypothetical situations and/or executing programs,or parts of programs, in data contexts relevant to the programmer.

2. Description of the Related Art

Programming is generally considered a difficult intellectual pursuit.The notion of programming precedes modern computers, but computers arethe main devices used to design, build and maintain programs. Computershave not only steadily become more powerful from a mere computationalcycles point of view, but over time they have also introduced andimproved new affordances relevant to programming, such as computergraphics, often used in visual programming, or new kinds of interfacedevices, such as the mouse, to enable more effective means of programcomposition, such as drag and drop interfaces. Naturally, most of theseefforts have focused on the most urgent problems in programming.Especially syntactic concerns have, in the early days of programming,and still today represented a difficult obstacle to overcome forbeginning programmers.

FIG. 1 conceptually illustrates the highly asymmetrical conversationbetween a programmer 100 and a programming environment 130 in thecontext of traditional programming. Communication between programmer 100and programming environment 130 is typically highly asymmetrical andlimited to syntactic feedback 110 of limited usefulness. Syntacticfeedback 110 is limited in nature to programs (e.g., program 140) thatare malformed. If one excludes one semicolon in a C program, the programmay not work at all. Over time, programming environments (e.g.,programming environment 130) have improved by integrating a number oftools that seek to help reduce problems stemming from syntactic issues.However, even modern integrated programming environments, such asEclipse, Xcode or Visual Studio largely follow the model of FIG. 1 byproviding a highly asymmetrical conversation of limited usefulness.

This highly asymmetrical conversation is based on a long history ofprogramming research and represents a crucial step in the rightdirection. Some of the first programming environments hardly includedany kind of meaningful feedback turning the process of programming intoa monologue. For example, early programmers had to enter a completeprogram without any kind of syntactic feedback. Then, when trying to runor compile the program, the programmer would see that the program didnot work or, in the best case scenario, get some error message from thecompiler. The main problem with such programming was recognized earlyon. Researchers trying to create programming environments then createdsystems that would provide earlier and more meaningful feedback. By1967, the Dialog system employed input/output devices that were manyyears ahead of its time and provided almost instant feedback to theprogrammer after each character was input in a way similar to modernsymbol completion found in Integrated Development Environments such asthe symbol completion on Lisp machines, or intellisense of Visual Studioand similar environments including Eclipse, and Xcode. All of theseenvironments analyze text input and help the programmer by eitherpopping up valid completion menus or a constrained set of characters andsymbols on a virtual keyboard.

A very different approach but with similar results can be found in thefield of visual programming. Instead of typing in text-basedinstructions many visual programming languages are based on thecomposition of programs from basic components using mechanisms such asdrag and drop. Similar to symbol completion approaches, these kinds ofvisual programming environments essentially attempt to prevent syntacticprogramming mistakes based on typos. Systems, such as AgentSheets,provide dynamic drag and drop feedback to indicatecompatibility/incompatibility of some programming language buildingblocks while the user is trying to drag them onto targets. Otherapproaches experiment with puzzle piece shaped programming languagebuilding blocks to convey compatibility. More recent systems aimed atend-users, such as Scratch, use similar approaches.

While these approaches have significantly reduced syntactic problemsthey largely ignore the much more problematic notion of semanticfeedback. The fact that a programming environment 130 provides thenecessary syntactic feedback 110 (responsive to the programmer 100editing the program 120) to help the programmer 100 create asyntactically correct program does not imply that the resulting programis meaningful or even runs at all. The asymmetry of the conversationbetween programmer 100 and programming environment 130 in FIG. 1 is notonly intended to indicate the amount of information flowing each way butalso the degree of usefulness to create a program that works and ismeaningful.

Semantics is about the meaning of a program, which of course is veryimportant. Unfortunately, existing semantic analysis is limited inusefulness because it usually separates programs from data and thereforeremoves important context necessary to provide truly useful feedback tothe programmer. For instance, semantic analysis can reveal that afunction is called with the wrong number of kind or parameters. The truemeaning of calling a function with actual parameters can only beexperienced when actually calling that function. Is a certain conditiontrue, will a certain function return the right value? These kinds ofquestions require a deep dynamic approach including data in the analysisof semantics.

SUMMARY

Methods and systems are described for providing rich semantic feedbackto programmers by executing programs, or parts of programs, in datacontexts relevant to the programmer. According to one embodiment, amethod is provided for communicating semantic information to aprogrammer. Software code associated with one or more of multipleprogramming building blocks is enabled to be concurrently edited andexecuted within a programming environment. A conversational programmingagent of the programming environment receives (i) information regardingthe programming building blocks and (ii) information indicative of acurrent situation relating to the programming building blocks. Theconversational programming agent evaluates the programming buildingblocks based on the current situation. Then, detection of one or morelogical errors in one or more of the programming building blocks isfacilitated by the conversational programming agent proactivelyproviding semantic feedback regarding those of the programming buildingblocks to which the current situation is relevant to the programmerbased on results of the evaluation.

Other features of embodiments of the present invention will be apparentfrom the accompanying drawings and from the detailed description thatfollows.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present invention are illustrated by way of example,and not by way of limitation, in the figures of the accompanyingdrawings and in which like reference numerals refer to similar elementsand in which:

FIG. 1 conceptually illustrates the highly asymmetrical conversationbetween a programmer and a programming environment in the context oftraditional programming.

FIG. 2 conceptually illustrates the rich semantic feedback provided by aconversational programming environment according to one embodiment ofthe present invention.

FIGS. 3A-3C illustrate various types of reactive changes in annotationsof latent programming language building blocks contained in a conditionspalette in accordance with an embodiment of the present invention.

FIGS. 4-5 illustrates evaluation of rules in a behavior editorresponsive to hypothetical situations depicted in a worksheet inaccordance with an embodiment of the present invention.

FIG. 6 illustrates how conversational programming may assist inconnection with finding programming bugs in accordance with anembodiment of the present invention.

FIGS. 7A-7B illustrates how operational blocks can be annotatedproactively in accordance with an embodiment of the present invention.

FIG. 8 is a flow diagram illustrating a high-level conversationalprogramming process in accordance with an embodiment of the presentinvention.

FIG. 9 is a flow diagram illustrating conversational programming agentprocessing in accordance with an embodiment of the present invention.

FIG. 10 is an exemplary computer system which may be used in accordancewith various embodiments of the present invention.

DETAILED DESCRIPTION

Methods and systems are described for providing rich semantic feedbackto programmers by executing programs, or parts of programs, in datacontexts relevant to the programmer. According to one embodiment,conversational programming provides rich semantic feedback toprogrammers by evaluating rules in a behavior editor responsive tohypothetical situations and/or executing programs, or parts of programs,in data contexts relevant to the programmer. In accordance with variousembodiments, a fundamental idea of conversational programming is toestablish a conversation that is much more symmetrical than traditionalprogramming environments by providing the programmer with semanticinformation at the right time in the right form. In its most extremeform conversational programming could be conceptualized as some kind ofchatty conversational programming agent acting as a pair programmingbuddy by constantly running the program created by the programmer. Indoing so, the conversational programming agent not only provides richsemantic information about the existing program, but may also evenproactively provide information about programs or portions thereof thathave not even been written by the programmer.

In the following description, numerous specific details are set forth inorder to provide a thorough understanding of embodiments of the presentinvention. It will be apparent, however, to one skilled in the art thatembodiments of the present invention may be practiced without some ofthese specific details. In other instances, well-known structures anddevices are shown in block diagram form.

While, for convenience, embodiments of the present invention aredescribed with reference to visual programming languages andspecifically with reference to AgentSheets (an agent-based simulationand game authoring tool available from AgentSheets, Inc of Boulder,Colo.), it is to be recognized that the principles of conversationalprogramming hold true for any kind of object-oriented or agent-basedcomputational system. Similarly, for sake of brevity and illustration,embodiments of the present invention are described with reference tocolored annotations relating to evaluation of Boolean conditions;however, the present invention is equally applicable to (i) more complexoutput, including but not limited to numbers, strings or anything afunction can return that could be presented textually or visually and(ii) alternative visual annotations, including but not limited to shape,texture, animation or other color schemes and/or use of sound or speech.

Embodiments of the present invention include various steps, which willbe described below. The steps may be performed by hardware components ormay be embodied in machine-executable instructions, which may be used tocause a general-purpose or special-purpose processor programmed with theinstructions to perform the steps. Alternatively, the steps may beperformed by a combination of hardware, software, firmware and/or byhuman operators.

Embodiments of the present invention may be provided as a computerprogram product, which may include a machine-readable storage mediumtangibly embodying thereon instructions, which may be used to program acomputer (or other electronic devices) to perform a process. Themachine-readable medium may include, but is not limited to, fixed (hard)drives, magnetic tape, floppy diskettes, optical disks, compact discread-only memories (CD-ROMs), and magneto-optical disks, semiconductormemories, such as ROMs, PROMs, random access memories (RAMs),programmable read-only memories (PROMs), erasable PROMs (EPROMs),electrically erasable PROMs (EEPROMs), flash memory, magnetic or opticalcards, or other type of media/machine-readable medium suitable forstoring electronic instructions (e.g., computer programming code, suchas software or firmware). Moreover, embodiments of the present inventionmay also be downloaded as one or more computer program products, whereinthe program may be transferred from a remote computer to a requestingcomputer by way of data signals embodied in a carrier wave or otherpropagation medium via a communication link (e.g., a modem or networkconnection).

In various embodiments, the article(s) of manufacture (e.g., thecomputer program products) containing the computer programming code maybe used by executing the code directly from the machine-readable storagemedium or by copying the code from the machine-readable storage mediuminto another machine-readable storage medium (e.g., a hard disk, RAM,etc.) or by transmitting the code on a network for remote execution.Various methods described herein may be practiced by combining one ormore machine-readable storage media containing the code according to thepresent invention with appropriate standard computer hardware to executethe code contained therein. An apparatus for practicing variousembodiments of the present invention may involve one or more computers(or one or more processors within a single computer) and storage systemscontaining or having network access to computer program(s) coded inaccordance with various methods described herein, and the method stepsof the invention could be accomplished by modules, routines,subroutines, or subparts of a computer program product.

Notably, while embodiments of the present invention may be describedusing visual programming terminology and/or modular programmingterminology, the code implementing various embodiments of the presentinvention is not so limited. For example, the code may reflect otherprogramming paradigms and/or styles, including, but not limited toobject-oriented programming (OOP), agent oriented programming,aspect-oriented programming, attribute-oriented programming (@OP),automatic programming, dataflow programming, declarative programming,functional programming, event-driven programming, feature orientedprogramming, imperative programming, semantic-oriented programming,functional programming, genetic programming, logic programming, patternmatching programming and the like.

TERMINOLOGY

Brief definitions of terms used throughout this application are givenbelow.

The terms “connected” or “coupled” and related terms are used in anoperational sense and are not necessarily limited to a direct connectionor coupling.

The phrases “in one embodiment,” “according to one embodiment,” and thelike generally mean the particular feature, structure, or characteristicfollowing the phrase is included in at least one embodiment of thepresent invention, and may be included in more than one embodiment ofthe present invention. Importantly, such phases do not necessarily referto the same embodiment.

The phrase “latent program” generally refers to a piece of a programincluding a collection of latent programming building blocks that areavailable to be used to compose an operational program. In someembodiments, the latent program includes palettes such as a conditionpalette and an action palette.

If the specification states a component or feature “may”, “can”,“could”, or “might” be included or have a characteristic, thatparticular component or feature is not required to be included or havethe characteristic.

The phrase “operational program” generally refers to the piece of aprogram that expresses the function of the project. According toembodiments of the present invention, this function may be a collectionof all the behaviors expressed as methods of agent classes.

The term “program” generally refers to instructions for a computer.Programs typically have a human-readable form (e.g., source code orvisual representation), from which an executable form is derived (e.g.,compiled) that the computer can use directly to execute theinstructions. A program can be split up into tow pieces. One called theoperational program and one called the latent program.

The term “responsive” includes completely or partially responsive.

The term “semantics” generally refers to an assumed or explicit set ofunderstandings used in a system to give meaning to data. According toone embodiment, semantic information provided to a programmer includescontext-dependent (e.g., situation-dependent) information regarding themeaning of a portion of a latent or operational program. For example,colored annotations relating to evaluation of Boolean conditionsassociated with programming building blocks (e.g., behavior rulesassociated with an agent) may be provided to a programmer to illustrateto the programmer which, if any, rules contained within a behavioreditor, for example, would “fire” responsive to a hypothetical situationdefined by the programmer within a worksheet, for example.Alternatively, more complex output, including, but not limited to,numbers, strings or anything a function can return that could bepresented textually or visually may be employed to provide semanticfeedback to a programmer. For example, alternative visual annotations,including but not limited to shape, texture, animation or other colorschemes and/or use of sound or speech may be used.

The term “situation” generally refers to a combination of a selectionand data. In some embodiments, the selection may refer to a specificinstance or instances of an agent or agents; whereas data may describethe collection of the agents. Data may manifest itself visibly to theuser (e.g., position, size or shape of an agent). Alternatively, data,such as the agent attributes, may not have a visible manifestation andonly becomes accessible to the user through tools, such as inspectors.

FIG. 2 conceptually illustrates rich semantic feedback 230 provided by aconversational programming environment 240 according to one embodimentof the present invention. In the conversational programming architectureof FIG. 2, the main difference from FIG. 1 is the presence of richsemantic feedback 230, which is provided by a Conversational ProgrammingAgent (CPA) 250. To make this kind of feedback possible, the CPA 250executes a program 260 and is provided with access to a combination ofdata and a selection called a situation 270.

The notion of conversation emerges from conversation taking placebetween the programmer 200 and the CPA 250. Similar to a naturallanguage conversation, it is desirable to achieve a workable balance ofcontributions by the participants (e.g., both the programmer 200 and theconversational programming agent 250) in the conversation. Inembodiments of the present invention, the programmer 200 edits/changes220 the program 260 and/or the situation 270. The CPA 250, in turn,executes the program 260, or selective parts of the program, in thecontext of the situation 270. According to one embodiment, the resultsof this execution are represented through annotations (not shown) in theprogram 260. A simple example is the execution of conditions, returninga value of either true or false. These results are used to annotate theconditions, for instance, red color may be used to indicate conditionsbeing false and green color may be used to indicate conditions beingtrue.

The large size of the semantic feedback 230 arrow pointing back at theprogrammer 200 reflects the rich nature of the conversational feedbackbeing provided. In accordance with embodiments of the present invention,even a relatively small conversation starter, such as the programmer 200applying a minor edit 220 to the program 260 and/or the situation 270,may result in potentially large amount of semantic feedback 230. Forinstance, just moving the frog in a Frogger like game a little bit mayresult in many annotation changes in the program 260.

Various components of the conversational programming environmentarchitecture as depicted in FIG. 2 are described in detail below. Wherenecessary and for convenience, AgentSheets is used as an illustration tomake examples more concrete (AgentSheets is an agent-based simulationand game authoring tool available from AgentSheets, Inc of Boulder,Colo.). Importantly, however, the principles of conversationalprogramming hold true for any kind of object-oriented or agent-basedcomputational system. Agents are autonomous types of objects that can beimplemented with any object-oriented system featuring some kindthreading mechanism.

According to the present example, the program 260 is split up into twopieces called the operational program (not shown) and the latent program(not shown). The operational program is the actual program expressingthe function of the project. This function is a collection of all thebehaviors expressed as methods of agent classes. Parts of theoperational program will be executed by the Conversational ProgrammingAgent (CPA) 250 in the context of the current situation 270. The resultsof these executions will be used by the CPA 250 to annotate theoperational program. The latent program is a collection of latentprogramming building blocks which could be used to compose anoperational program. In the context of AgentSheets, the latent programconsists of the palettes including the condition and action palette.

The situation 270 describes the combination of data and selection. Data,similar to the notion of data in a spreadsheet, describes the collectionof all agents in worksheets, called agentsheets in the context ofAgentSheets. Most of the data manifests themselves visibly to the user,such as the position, size or shape an agent. Other data, such as theagent attributes, may not have a visible manifestation and only becomeaccessible to the user through tools such as inspectors. A selection isbased on zero, one or more agents designated by user for instancethrough a mouse click. The selection may indicate, for instance, to theinspector which agent is going to be inspected. The combination ofselection and data results in a situation 270 providing the situatedtopic of the conversation. A selection refers to specific instances ofagents, for instance, the frog in a Frogger game. The selected agentalso determines the class of the selected agent, which in turn, furtherfocuses conversational programming to the part of the main programdefining the behavior of an agent. For instance, selecting the frogmeans that one can focus on the frog's behavior.

In embodiments of the present invention, the CPA 250 converses with theprogrammer 200 by annotating latent and operational programming languagebuilding blocks by executing them in the context of the currentsituation 270. The programmer 200 converses with the CPA 250 by changingthe situation 270, e.g., editing 220 data or changing the selection, byediting/changing 220 the program 260 or by changing parameters of theprogramming language building blocks.

Latent programming language building blocks are building blocks that arenot part of the operational program, but which could become part of theoperational program through programming initiated by the programmer 200through a compositional process such as drag and drop. All theprogramming language building blocks contained in the condition andaction palettes of AgentSheets, for instance, are latent programminglanguage building blocks. The conversational programming agent 250annotates latent programming language building blocks, as describedfurther below, indicating that certain building blocks exhibit semanticrelevance to particular situations.

Operational programming language building blocks are part of theoperational program. Similar to the latent programming language buildingblocks, the conversational programming agent 250 executes operationalprogramming language building blocks in the context of the currentsituation 270 and annotates these blocks.

Examples are now provided to illustrate the function of conversationalprogramming. Unfortunately, the intrinsically static nature of paperused to represent these examples is hard pressed to convey theintrinsically dynamic nature of conversational programming. Four maincases can be differentiated based on two fundamental attributes ofconversational programming. The type of programming language buildingblocks is either latent or operational. The conversation taking placebetween the programmer 200 and the conversational programming agent 250is either reactive or proactive. The following sections describe allfour combinations of these attributes.

FIGS. 3A-3C illustrate various types of reactive changes in annotations321 and 331 of latent programming language building blocks contained ina conditions palette 315 in accordance with an embodiment of the presentinvention. In the examples depicted in FIGS. 3A-3C, a Frogger-like gamehas been built with AgentSheets. The programmer has created a number ofagents including a frog 304, trucks (e.g., truck 302), roads (e.g., road301) and ground 303. In the present example, none of these agents havebeen programmed yet. Rather, in the context of FIGS. 3A-3C, theprogrammer has created situations 310, 320 and 330 representing the gameworld and has made the frog 304 to be the current selection. As such,FIG. 3A-3C show the reactive change in annotations 321 and 331 of thelatent programming language building blocks contained in the AgentSheetsconditions palette 315.

In FIG. 3A (situation 310), the frog 304 is about to cross the street.The first condition 320 (i.e., “see left, truck”) is false. The secondcondition 330 (i.e., “stacked immediately above the ground”) is true.Information about these conditions 320 and 330 can be conveyed to theprogrammer by presenting the conditions 320 and 330 in color, forexample, corresponding with the Boolean value of the result 321 and 330.For example, false conditions may be presented in red and trueconditions may be presented in green. Those skilled in the art willrecognize various alternative mechanisms are available for visuallycommunicating such semantic information. For example, true conditionsmay be presented more prominently than false conditions or trueconditions may be presented in color and false conditions may be gray.Notably, while embodiments are described with reference to coloredannotations 321 and 331 relating to evaluation of Boolean conditions,those skilled in the art will appreciate the broader applicability ofthe present invention to (i) more complex output, including but notlimited to numbers, strings or anything a function can return that couldbe presented textually or visually and (ii) alternative visualannotations, including but not limited to shape, texture, animation orother color schemes and/or use of sound or speech as auditoryannotations.

In FIG. 3B (situation 320), the frog 304 is on the street next to atruck 302. In this situation 320, the first condition 320 (i.e., “seeleft, truck”) is true. The second condition 330 (i.e., “stackedimmediately above the ground”) is false. Again, information about theseconditions 320 and 330 can be conveyed to the programmer by graphicallypresenting the evaluated conditions in a distinguishing manner inaccordance with the evaluated results (e.g., Boolean value of the result321 and 330 and/or color of conditions 320 and 330).

In FIG. 3C (situation 330), the frog 304 is on the street 301 without atruck heading towards it. In this situation 330, the first condition 320(i.e., “see left, truck”) is false. The second condition 330 (i.e.,“stacked immediately above the ground”) is false. Again, informationabout these conditions can be conveyed to the programmer by graphicallypresenting the evaluated conditions 320 and 330 in a distinguishingmanner in accordance with the evaluated results (e.g., Boolean value ofthe result 321 and 330 and/or color of conditions 320 and 330).

The process used to get into a certain situation (e.g., situations 310,320 and 330) is irrelevant in conversational programming. Perhaps thefrog 304 has not yet been programmed at all, i.e., it only has emptymethods, or perhaps the frog 304 does feature a simple version of aprogram, such as a method that will move the frog around as the resultof a user pressing cursor keys. How the frog 304 got to where it is,however, is irrelevant to the semantic information (e.g., semanticfeedback 230) resulting from evaluation of the situation (e.g.,situations 310, 320 and 330). For example, the operational program maybe running or may be stopped and the programmer simply has used themouse to drag the frog 304 from one location to another. None of this isrelevant to the conversation. The same kind of semantic feedback (e.g.,Boolean value of the result 321 and 330 and/or color of conditions 320and 330) may provided regardless of the process used to create thecurrent situation (e.g., situations 310, 320 and 330).

While some end-user programming tools allow a programmer to test acondition of interest, conversational programming substantially improveson these prior approaches in at least two ways. Firstly, using aconversational programming approach, the programmer does not need to askfor this kind of feedback. Instead, the system reacts immediately to achange in situation (e.g., situation 270). Secondly, the programmer doesnot have to select a specific language building block to be evaluated.Instead, in embodiments of the present invention, all language blocksrelevant to the situation may be annotated automatically by the CPA 250.At the very least, this is an improvement of efficiency. A programmercould sequentially select all the conditions in the condition paletteand repeat to test; however, this could take a long time (e.g.,AgentSheets include about 30 different conditions). The conversationalprogramming agent 250, conceptually speaking, can annotate all thebuilding blocks in parallel. This approach is not only much faster butit also increases the potential of serendipitous discovery. After all, aprogrammer may not even be aware that a certain programming languagebuilding block exists, or that it includes relevant semantics. Based ontiming alone, the ability to quickly change the situation (e.g.,situation 270) and to almost immediately perceive semantic consequencescould result in the perception of causality in a way that would not bepossible with previous mechanisms.

Unlike the reactive version of conversational programming, proactiveconversations do not have to wait for some kind of stimulus by theprogrammer. That is, the situation (e.g., situation 270) may not changeand the program (e.g., program 260) may remain the same, but timeadvances. The proactive nature of conversational programming enables adifferent kind of autonomy where the conversational programming agent250 can, at any point in time, initiate a conversation to conveypotentially important information. For instance, AgentSheets includes anumber of temporal and probabilistic language building blocks whichbenefit from conversational programming treatment.

As one example of a temporal language building block in the context ofAgentSheets, the “OnceEvery” condition provides a means to accesstimers. A condition may be specified in such a manner as to be true onlyonce every 1.0 seconds, for example. The conversational programmingprocess can make the OnceEvery condition blink at the frequencyexpressed by the time parameter. In this manner, a programmer is able toexperience the function of the OnceEvery command dynamically includingits frequency in way that is very different from accessing static mediasuch as tool tips. Moreover, this means of communicating semanticsincludes concrete values. For instance, if the time parameter ofOnceEvery is 0.5 instead of 1.0 the programmer would “see” that thecondition is true at a frequency twice as large as with 1.0. Even if theparameter is an expression instead of just a constant value, theprogrammer would be able to experience the value of that expression.

As one example of a probabilistic language building block in the contextof AgentSheets, a “% Chance” condition will be true or false based onthe probability specified by its parameter. Conversational programmingcan be used to communicate that probability through proactive annotationof the condition. For instance, a 50% chance condition can be displayedso as to flicker in a way being green or red at a 50/50 percentdistribution. A high probability of, say, 95% can result in a % chancecondition mostly being green, whereas, a low probably of, say, 5% willbe displayed red most of the time.

FIGS. 4-5 illustrates evaluation of rules 415 in a behavior editor 410responsive to hypothetical situations depicted in a worksheet 420 inaccordance with an embodiment of the present invention. As indicatedabove, operational programming language building blocks are part of themain operational program. In AgentSheets, and most object-orientedframeworks, they are contained in methods which are associated withclasses. Like their latent counterparts, operational programminglanguage blocks participate in the conversational programming process byconstantly being evaluated and the results of that evaluation are usedto annotate the building block. However, because these building blocksare part of more complex program structures and not just an independentcollection of building blocks, such as the ones in a palette, theconsequence of evaluating these operational programming languagebuilding blocks can be traced further. For instance, if a condition isfound to be false, then one can analyze parts of the program containingthe condition. If the condition is part of some complex IF-THEN or CASEexpression, one can annotate all consequences of conditions being trueor false.

In FIG. 4, the behavior of the frog 421 is represented in VisualAgenTalk rules (e.g., rule 415), which in most traditional languages,such as Java would be IF <conditions>, THEN <actions> ELSEIF<conditions> THEN <actions>, ELSE IF <conditions> THEN <actions>, . .. . According to the present example, each rule (e.g., rule 417) has oneor more conditions 411 and one or more actions 412.

Notice in the agentsheet, called “Worksheet: level1” (FIG. 4, right) thefrog 421 is selected. In this situation, the frog 421 is on top ofwater, which, according to the original game play description of Froggeris bad news for the frog 421 as it cannot swim. The first rule 415 ofthe behavior editor 410 (FIG. 4, left) has a Stacked condition 413,which is true because the frog is indeed “immediately above” water. Notonly may the condition 413, the programming language building block, beannotated in green, for example, to indicate that the condition 413 istrue, but the entire first rule 415 may feature a green background, forexample. The meaning is that all of the conditions of this rule 415(just one in this case—condition 413) are true so that the rule could“fire” by executing all its actions (e.g., action 414).

According to embodiments of the present invention, conversationalprogramming does not cause side effects. The combination of worksheet420 and behavior editor 410 interact such that in the current situationthe first rule 415 would fire causing the frog 421 to perform thecorresponding actions 414 (i.e., say that it cannot swim and thendisappear). Importantly, this is not some kind of runtime tracing toolvisualizing the execution of the program. In the context of the presentexample, conversational programming only shows that in the currentsituation the frog 421 would drown, but does not actually drown it. Thisis part of a conversation about a hypothetical situation that the useris playing around with or testing without running the main program. Ofcourse, the programmer can run the main program which would cause thefrog to drown.

Other rules including their conditions are not annotated in the presentexample because the particular rule semantics of AgentSheets is that:all rules are tested from top to bottom; the first rule 417 with all itsconditions 411 being true will fire; rules following the rule that isfiring will not be tested. The annotation in FIG. 4 stops at the firstrule because of this.

In FIG. 5, the situation is changed by moving the frog 421 down to theroad in front of a truck. Once again, the situation for the frog 421looks bleak. Now, the annotation may tell a different story, forexample, by coloring rule 413 green. Rule evaluation, again, is handledtop to bottom. The frog 421 is NOT stacked above water, therefore thefirst rule 431 fails. The frog is not stacked on top of the groundeither, therefore the second rule 432 also fails. However, the frog 421does see, to its left, a truck. Thus, the third rule 413 does fire, asound is played, the frog 421 turns into a crushed looking frog andafter some brief delay the frog disappears and the simulation is reset.

FIG. 6 illustrates how conversational programming may assist inconnection with finding programming bugs in accordance with anembodiment of the present invention. Conversational programming isuseful to find programming bugs. The previous examples are mostlyshowing what well designed programs would do if they were run, but inmany cases one wishes to investigate why an existing program is notworking. FIG. 6 shows a Sims-like game in which various signals, such asfood 622 and social values (not shown), are diffused as complexlandscapes over the worksheet 620.

As in the examples described above, according to the present example,each rule (e.g., rule 618) in behavior editor 610 has one or moreconditions 611 and one or more actions 612.

The selected agent 621 in FIG. 6 is trying to find the food 622, but isstuck in a maze. Looking at the situation suggest that the agent 621should be moving to the right, towards the hamburger 622, but the rule625 responsible for moving to the right does not fire. In this example,the method containing the rule 625 is a hill climbing method making theagent 621 look at its four immediate neighbors, comparing their valuesand finally moving in the direction of maximal value.

Even without looking at the precise expressions used in the conditionsof the hill climbing method, one can find that the second condition 616of the rule 625 that would make the agent go to the right is false (andmay be annotated by coloring the entire rule, the condition 616 and/orthe actions red). Closer inspection reveals that the second condition616 is incorrectly (accidentally) comparing the value of the leftneighbor with the value of the down neighbor instead of comparing thevalue of the right neighbor with the value of the down neighbor. Thiscould have easily been the result of copying the first rule 624. Thesemantic feedback provided by conversational programming helps toquickly focus the investigation on the faulty condition which is likelyto simplify the debugging process.

FIGS. 7A-7B illustrates how operational blocks 730 and 740 can beannotated proactively in accordance with an embodiment of the presentinvention. Just like latent programming language building blocks,operational blocks (e.g., 730 and 740) can be annotated proactively. Inother words, there is no need for a programmer to produce a new stimulusin order to receive a new response (updated semantic information fromthe conversational programming environment). Proactive conversations canbe instrumental in conveying difficult semantics especially whenconnected to temporal and probabilistic matters. For instance, theconsequences of order of execution especially in conditional expressionsis notoriously difficult to understand for beginning programmers andsometimes even for experienced programmers.

In a first rule 730 depicted in FIG. 7A, two conditions 711 and 712 areemployed to make an agent move. Let's assume that the rule 730 is beingevaluated at high frequency. For instance, in a video game it would becommon to execute some object behavioral scripts at the same frequencyas the game is rendered graphically, e.g., 60 frames per second. Thefirst rule 730 would first test the OnceEvery (1.0) second condition 71160 times per second, which would be true only once per second. Thus, thesecond condition 712, the % chance (50) condition, would be tested onlyonce per second resulting in the rule 730 only firing once every othersecond. A programmer may be alerted to this by, for example, the entirerule 730 flashing green at a rate of once every two seconds.

The second rule 740 depicted in FIG. 7B would behave very differently.Its first condition 721, the % chance(50) condition, assuming it isevaluated 60 times per second, would be true on average roughly 30 timesper second. If and only if the % chance condition 721 is true, thesystem would test the second condition 722, i.e., the OnceEvery (1.0)timer condition. This condition 722 is only true once per second. Beingtested at about 30 times per second, the rule 740 would fire once persecond. A programmer would be alerted to this, for example, by theentire rule 740 flashing green at this much higher frequency—once persecond.

FIG. 8 is a flow diagram illustrating a high-level conversationalprogramming process in accordance with an embodiment of the presentinvention. In the present example, the conversational programmingprocess begins by receiving either information regarding the occurrenceof a reactive event (block 810) or information regarding the occurrenceof a proactive event (block 820). In one embodiment, the reactive eventmay represent one or more of (i) editing of a situation (either the dataor the selection) by an end user (e.g., a programmer); (ii) editing ofone or more latent programming building blocks of a latent program; and(iii) editing of one or more operational programming building blocks ofan operational program. The proactive event, may represent one or moretypes of events that do not require stimulus by the end user. Examplesof proactive events include, but are not limited to, periodic evaluationof conditions relating to probability (e.g., % chance conditions) orconditions relating to time (e.g., OnceEvery conditions).

Responsive to receiving information regarding the occurrence of areactive event or a proactive event, at block 830, conversationalprogramming agent processing is performed. According to one embodiment,a conversational programming agent of a conversational programmingenvironment has access to both programming building blocks (includinglatent and operational programming building blocks) and context providedby way of a situation, including data and selection information. Assuch, the conversational programming agent may interpret the situationand provide semantic feedback to the end user by executing/evaluatingrelevant programming building blocks of a latent or operational programand appropriately annotating the relevant programming building blocksbased on the results of the evaluation.

FIG. 9 is a flow diagram illustrating conversational programming agentprocessing in accordance with an embodiment of the present invention.According to the present example, conversational programming agentprocessing begins with block 910. At block 910, a conversationalprogramming agent of a conversational programming environment receivesinformation regarding available program building blocks and informationindicative of an associated current situation.

In one embodiment, the available program building blocks include latentprogramming building blocks and/or operational programming buildingblocks. In the context of a program that is yet to be completed or anagent having empty methods, incomplete methods or simplified methods, aprogrammer may experiment with different hypothetical situations withina conditions palette containing one or more conditions under test. Insome embodiments, the conversational programming agent identifiesrelevant programming building blocks based on a selection associatedwith the current situation. For example, in FIGS. 3A-3C, the frog 304represents the selected agent. As such, the conversational programmingagent may identify rules associated with the frog agent as those to beevaluated and annotated.

At block 920, the conversational programming agent evaluates therelevant programming building blocks in view of the current situation.According to one embodiment, evaluating the programming building blocksinvolves interpreting the data and selection associated with thesituation established by the end user and evaluating one or morefunctions or conditions of rules based on the current situation. Asnoted above with reference to FIGS. 4 and 5, evaluation of relevantprogramming building blocks may involve evaluating conditions within abehavior editor based on a situation represented within a worksheet.

In one embodiment, the end user may enable or disable evaluation oflatent programming building blocks and thus removing them from the setof relevant programming building blocks. In other embodiments, theconversational programming agent may perform a relevance assessment withrespect to one or both of latent and operational programming buildingblocks to determine which are to be evaluated. As indicated above, forexample, when the frog is the selected agent in the situation, theconversational programming agent may infer that the end user would likesemantic feedback regarding rules associated with the frog agent. Inother embodiments, rules associated with all agents involved in thesituation may be evaluated.

At block 930, the latent and/or operational program is annotated basedon results of the evaluation. In one embodiment, rules within a behavioreditor, for example, that would “fire” in the context of the situationrepresented within a worksheet, for example, may be annotated bypresenting them with a colored, e.g., green background. Alternatively,the output of a function (e.g., a Boolean condition) can be providedproximate to the rule or condition in the behavior editor. Similarly,conditions evaluating to true could be visually depicted in one mannerand Boolean conditions evaluating to false could be visually depicted inanother manner to allow the end user to distinguish among them.

As noted above, more complex annotation output is contemplated. Forexample, numbers, strings or anything a function can return that couldbe presented textually or visually could be displayed proximate to therule or condition at issue. Various other visual annotations, includingbut not limited to shape, texture, animation or other color schemesand/or use of sound or speech as auditory annotations may also be usedalone or in combination with other annotation mechanisms describedherein.

FIG. 10 is an exemplary computer system which may be used in accordancewith various embodiments of the present invention. Embodiments of thepresent invention include various steps, which have been describedabove. A variety of these steps may be performed by hardware componentsor may be tangibly embodied on a computer-readable storage medium in theform of machine-executable instructions, which may be used to cause oneor more general-purpose or special-purpose computer processors ormicroprocessors programmed with instructions to perform these steps.Alternatively, the steps may be performed by a combination of hardware,software, and/or firmware. As such, FIG. 10 is an example of a computersystem 1000, such as a workstation, personal computer, laptop, client orserver, upon which or with which embodiments of the present inventionmay be employed.

According to the present example, the computer system includes a bus1030, one or more processors 1005, one or more communication ports 1010,a main memory 1015, a removable storage media 1040, a read only memory1020 and a mass storage 1025.

Processor(s) 1005 can be any future or existing processor, including,but not limited to, an Intel® Itanium® or Itanium 2 processor(s), orAMD® Opteron® or Athlon MP® processor(s), or Motorola® lines ofprocessors. Communication port(s) 1010 can be any of an RS-232 port foruse with a modem based dialup connection, a 10/100 Ethernet port, aGigabit port using copper or fiber or other existing or future ports.Communication port(s) 1010 may be chosen depending on a network, such aLocal Area Network (LAN), Wide Area Network (WAN), or any network towhich the computer system 1000 connects.

Main memory 1015 can be Random Access Memory (RAM), or any other dynamicstorage device(s) commonly known in the art. Read only memory 1020 canbe any static storage device(s) such as Programmable Read Only Memory(PROM) chips for storing static information such as start-up or BIOSinstructions for processor 1005.

Mass storage 1025 may be any current or future mass storage solution,which can be used to store information and/or instructions. Exemplarymass storage solutions include, but are not limited to, ParallelAdvanced Technology Attachment (PATA) or Serial Advanced TechnologyAttachment (SATA) hard disk drives or solid-state drives (internal orexternal, e.g., having Universal Serial Bus (USB) and/or Firewireinterfaces), such as those available from Seagate (e.g., the SeagateBarracuda 7200 family) or Hitachi (e.g., the Hitachi Deskstar 7K1000),one or more optical discs, Redundant Array of Independent Disks (RAID)storage, such as an array of disks (e.g., SATA arrays), available fromvarious vendors including Dot Hill Systems Corp., LaCie, NexsanTechnologies, Inc. and Enhance Technology, Inc. According to oneembodiment, mass storage 1025 has tangibly embodied thereoninstructions, which when executed by processor(s) 1005, cause aninstance of the conversational programming agent of FIG. 2 to beinstantiated to execute a program at issue, interpret a situation atissue and annotate the program to provide semantic feedback to aprogrammer as described above.

Bus 1030 communicatively couples processor(s) 1005 with the othermemory, storage and communication blocks. Bus 1030 can include a bus,such as a Peripheral Component Interconnect (PCI)/PCI Extended (PCI-X),Small Computer System Interface (SCSI), USB or the like, for connectingexpansion cards, drives and other subsystems as well as other buses,such a front side bus (FSB), which connects the processor(s) 1005 tosystem memory.

Optionally, operator and administrative interfaces, such as a display,keyboard, and a cursor control device, may also be coupled to bus 1030to support direct operator interaction with computer system 1000. Otheroperator and administrative interfaces can be provided through networkconnections connected through communication ports 1010.

Removable storage media 1040 can be any kind of external hard-drives,floppy drives, IOMEGA® Zip Drives, Compact Disc—Read Only Memory(CD-ROM), Compact Disc—Re-Writable (CD-RW), Digital Video Disk—Read OnlyMemory (DVD-ROM).

Components described above are meant only to exemplify variouspossibilities. In no way should the aforementioned exemplary computersystem limit the scope of the invention.

What is claimed is:
 1. A method comprising: allowing software codeassociated with one or more of a plurality of programming buildingblocks to be concurrently edited and executed within a programmingenvironment, including: receiving, by a conversational programming agentof the programming environment, (i) information regarding the pluralityof programming building blocks and (ii) information indicative of acurrent situation relating to the plurality of programming buildingblocks; and evaluating, by the conversational programming agent, theplurality of programming building blocks based on the current situation;facilitating detection of one or more logical errors in one or more ofthe plurality of programming building blocks by proactively providing,by the conversational programming agent, semantic feedback regardingthose of the plurality of programming building blocks to which thecurrent situation is relevant to an end user based on the evaluating;and wherein the conversational programming agent is implemented in oneor more processors and one or more computer-readable media of one ormore computer systems, the one or more computer-readable media havinginstructions tangibly embodied therein that are executable by the one ormore processors.
 2. The method of claim 1, wherein the semantic feedbackcomprises annotating one or more of the plurality of programmingbuilding blocks.
 3. The method of claim 2, wherein the semantic feedbackcomprises colored annotations relating to evaluation of Booleanconditions associated with the plurality of programming building blocks.4. The method of claim 1, wherein the plurality of programming buildingblocks include operational programming building blocks.
 5. The method ofclaim 1, wherein the plurality of programming building blocks includelatent programming building blocks.
 6. The method of claim 1, whereinthe current situation includes data describing a collection of agentsinstantiated by an operational program.
 7. The method of claim 6,wherein the plurality of programming building blocks include operationalprogramming building blocks of the operational program associated with aselected agent of the collection of agents.
 8. The method of claim 1,further comprising concurrently displaying, by the programmingenvironment, (i) a visual representation of a programming building blockof the plurality of programming building blocks within a behavior editorand (ii) a visual representation of the current situation, wherein thecurrent situation includes (a) a selection identifying a selected agentof a plurality of agents represented by the plurality of programmingbuilding blocks and (b) data describing at least the selected agent andwherein the programming building block corresponds to the selected agentand represents one or more behavioral rules of the selected agent. 9.The method of claim 8, further comprising receiving from the end uservia interaction with the visual representation of the current situation,by the programming environment, input regarding a hypothetical situationto be used as the current situation for evaluation by the conversationalprogramming agent.
 10. A non-transitory computer-readable storage mediumtangibly embodying a set of instructions, which when executed by one ormore processors of one or more computer systems, cause the one or moreprocessors to perform a method comprising: allowing software codeassociated with one or more of a plurality of programming buildingblocks to be concurrently edited and executed within a programmingenvironment, including: receiving (i) information regarding theplurality of programming building blocks and (ii) information indicativeof a current situation relating to the plurality of programming buildingblocks; and evaluating the plurality of programming building blocksbased on the current situation; and facilitating detection of one ormore logical errors in one or more of the plurality of programmingbuilding blocks by proactively providing semantic feedback regardingthose of the plurality of programming building blocks to which thecurrent situation is relevant to an end user based on the evaluating.11. The non-transitory computer-readable storage medium of claim 10,wherein the semantic feedback comprises annotating one or more of theplurality of programming building blocks.
 12. The non-transitorycomputer-readable storage medium of claim 11, wherein the semanticfeedback comprises colored annotations relating to evaluation of Booleanconditions associated with the plurality of programming building blocks.13. The non-transitory computer-readable storage medium of claim 10,wherein the plurality of programming building blocks include operationalprogramming building blocks.
 14. The non-transitory computer-readablestorage medium of claim 10, wherein the plurality of programmingbuilding blocks include latent programming building blocks.
 15. Thenon-transitory computer-readable storage medium of claim 10, wherein thecurrent situation includes data describing a collection of agentsinstantiated by an operational program.
 16. The non-transitorycomputer-readable storage medium of claim 15, wherein the plurality ofprogramming building blocks include operational programming buildingblocks of the operational program associated with a selected agent ofthe collection of agents.
 17. The non-transitory computer-readablestorage medium of claim 10, wherein the method further comprisesconcurrently displaying (i) a visual representation of a programmingbuilding block of the plurality of programming building blocks within abehavior editor and (ii) a visual representation of the currentsituation, wherein the current situation includes (a) a selectionidentifying a selected agent of a plurality of agents represented by theplurality of programming building blocks and (b) data describing atleast the selected agent and wherein the programming building blockcorresponds to the selected agent and represents one or more behavioralrules of the selected agent.
 18. The non-transitory computer-readablestorage medium of claim 17, wherein the method further comprisesreceiving from the end user via interaction with the visualrepresentation of the current situation, by the programming environment,input regarding a hypothetical situation to be used as the currentsituation for evaluation by the conversational programming agent.
 19. Acomputer-implemented method comprising: allowing software codeassociated with one or more of a plurality of programming buildingblocks to be concurrently edited and executed within a programmingenvironment, including concurrently displaying, by the programmingenvironment, (i) a visual representation of a programming building blockof the plurality of programming building blocks within a behavior editorand (ii) a visual representation of a current situation, wherein thecurrent situation includes (a) a selection identifying a selected agentof a plurality of agents represented by the plurality of programmingbuilding blocks and (b) data describing at least the selected agent andwherein the programming building block corresponds to the selected agentand represents one or more behavioral rules of the selected agent;responsive to selection of the selected agent by an end user of theprogramming environment, a conversational programming agent of theprogramming environment, evaluating, based on the current situation, theone or more behavior rules and other behavior rules of one or more otherof the plurality of agents to which the current situation is relevant;and facilitating detection of one or more logical errors in the one ormore behavior rules by the end user by conveying results of saidevaluating, by the conversational programming agent, in a form of one ormore annotation to the visual representation of the programming buildingblock, wherein the one or more annotations provide semantic feedback tothe end user regarding a meaning of the one or more behavior rules in acontext of the current situation.
 20. The method of claim 19, whereinthe one or more behavior rules comprise Boolean conditions and whereinthe one or more annotations comprise colored annotations indicative oftruth or falsity of the Boolean conditions.